Viewing the proteome: How to visualize proteomics data?

Proteomics

Volumn 15, Issue 8, 2015, Pages 1341-1355

  Oveland, Eystein ^a   Muth, Thilo ^b,c   Rapp, Erdmann ^b,c   Martens, Lennart ^d,e   Berven, Frode S ^a,f   Barsnes, Harald ^a  

^a UNIVERSITY OF BERGEN   (Norway)

^b MAX PLANCK INSTITUTE FOR DYNAMICS OF COMPLEX TECHNICAL SYSTEMS   (Germany)

^c GlyXera GmbH   (Germany)

^d DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

^e GHENT UNIVERSITY   (Belgium)

^f HAUKELAND UNIVERSITY HOSPITAL   (Norway)

Author keywords

Bioinformatics; Graphs; Proteome databases; Raw data; Visualization

Indexed keywords

PEPTIDE; PROTEIN; PROTEOME;

AMINO ACID SEQUENCE; CHROMOSOME; HUMAN; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN INTERACTION; PROTEIN LOCALIZATION; PROTEOMICS; QUALITY CONTROL; QUANTITATIVE ANALYSIS; REVIEW; ANTIBODY SPECIFICITY; CHEMISTRY; COMPUTER GRAPHICS; COMPUTER PROGRAM; INFORMATION PROCESSING; METABOLISM; MOLECULAR GENETICS; PROCEDURES; PROTEIN DATABASE; PROTEIN PROTEIN INTERACTION; STATISTICAL ANALYSIS;

COMPUTER GRAPHICS; DATA INTERPRETATION, STATISTICAL; DATABASES, PROTEIN; DATASETS AS TOPIC; HUMANS; MOLECULAR SEQUENCE ANNOTATION; ORGAN SPECIFICITY; PROTEIN INTERACTION MAPS; PROTEOME; PROTEOMICS; SOFTWARE;

EID: 84926526302     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201400412     Document Type: Review

References (90)

1. Martin, S. F., Falkenberg, H., Dyrlund, T. F., Khoudoli, G.A. et al., PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development. J. Proteomics 2013, 88, 41-46.
2. Pan, S., Aebersold, R., Chen, R., Rush, J. et al., Mass spectrometry based targeted protein quantification: methods and applications. J. Proteome Res. 2009, 8, 787-797.
3. Gallien, S., Duriez, E., Domon, B., Selected reaction monitoring applied to proteomics. J. Mass Spectrom. 2011, 46, 298-312.
4. Kelchtermans, P., Bittremieux, W., De Grave, K., Degroeve, S. et al., Machine learning applications in proteomics research: how the past can boost the future. Proteomics 2014, 14, 353-366.
5. Verheggen, K., Barsnes, H., Martens, L., Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory. Proteomics 2014, 14, 367-377.
6. Deutsch, E., mzML: a single, unifying data format for mass spectrometer output. Proteomics 2008, 8, 2776-2777.
7. Jones, A. R., Eisenacher, M., Mayer, G., Kohlbacher, O. et al., The mzIdentML data standard for mass spectrometry-based proteomics results. Mol. Cell Proteomics 2012, 11, M111. 014381.
8. O'Donoghue, S. I., Gavin, A. C., Gehlenborg, N., Goodsell, D. S. et al., Visualizing biological data-now and in the future. Nat. Methods 2010, 7, S2-S4.
9. Gehlenborg, N., O'Donoghue, S. I., Baliga, N. S., Goesmann, A. et al., Visualization of omics data for systems biology. Nat. Methods 2010, 7, S56-S68.
10. Dang, X., Scotcher, J., Wu, S., Chu, R. K. et al., The first pilot project of the consortium for top-down proteomics: a status report. Proteomics 2014, 14, 1130-1140.
11. Alexandrov, T., MALDI imaging mass spectrometry: statistical data analysis and current computational challenges. BMC Bioinformatics 2012, 13, S11.
12. Gessel, M. M., Norris, J. L., Caprioli, R. M., MALDI imaging mass spectrometry: spatial molecular analysis to enable a new age of discovery. J. Proteomics 2014, 107C, 71-82.
13. Hyung, S. J., Ruotolo, B. T., Integrating mass spectrometry of intact protein complexes into structural proteomics. Proteomics 2012, 12, 1547-1564.
14. McHugh, L., Arthur, J. W., Computational methods for protein identification from mass spectrometry data. PLoS Comput. Biol. 2008, 4, e12.
15. Perez-Riverol, Y., Wang, R., Hermjakob, H., Muller, M. et al., Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective. Biochim. Biophys. Acta 2014, 1844, 63-76.
16. Hoopmann, M. R., Moritz, R. L., Current algorithmic solutions for peptide-based proteomics data generation and identification. Curr. Opin. Biotechnol. 2013, 24, 31-38.
17. Vaudel, M., Sickmann, A., Martens, L., Current methods for global proteome identification. Expert. Rev. Proteomics 2012, 9, 519-532.
18. Martens, L., Nesvizhskii, A.I., Hermjakob, H., Adamski, M. et al., Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories. Proteomics 2005, 5, 3501-3505.
19. Martens, L., Chambers, M., Sturm, M., Kessner, D. et al., mzML-a community standard for mass spectrometry data. Mol. Cell Proteomics 2011, 10, R110. 000133.
20. Chambers, M. C., Maclean, B., Burke, R., Amodei, D. et al., A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotech. 2012, 30, 918-920.
21. Tabb, D. L., Quality assessment for clinical proteomics. Clin. Biochem. 2013, 46, 411-420.
22. Walzer, M., Pernas, L. E., Nasso, S., Bittremieux, W. et al., qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol. Cell Proteomics 2014, 13, 1905-1913.
23. Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P., ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 2008, 24, 2534-2536.
24. Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M., MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 2010, 11, 395.
25. Keller, B. O., Sui, J., Young, A. B., Whittal, R. M., Interferences and contaminants encountered in modern mass spectrometry. Anal. Chim. Acta 2008, 627, 71-81.
26. Palagi, P. M., Walther, D., Quadroni, M., Catherinet, S. et al., MSight: an image analysis software for liquid chromatography-mass spectrometry. Proteomics 2005, 5, 2381-2384.
27. Sturm, M., Bertsch, A., Gropl, C., Hildebrandt, A. et al., OpenMS-an open-source software framework for mass spectrometry. BMC Bioinformatics 2008, 9, 163.
28. Wenig, P., Odermatt, J., OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data. BMC Bioinformatics 2010, 11, 405.
29. Barsnes, H., Vaudel, M., Colaert, N., Helsens, K. et al., compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics 2011, 12, 70.
30. Wang, R., Fabregat, A., Rios, D., Ovelleiro, D. et al., PRIDE inspector: a tool to visualize and validate MS proteomics data. Nat. Biotechnol. 2012, 30, 135-137.
31. Chambers, M. C., Maclean, B., Burke, R., Amodei, D. et al., A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30, 918-920.
32. Barsnes, H., Huber, S., Sickmann, A., Eidhammer, I., Martens, L., OMSSA Parser: an open-source library to parse and extract data from OMSSA MS/MS search results. Proteomics 2009, 9, 3772-3774.
33. Muth, T., Vaudel, M., Barsnes, H., Martens, L., Sickmann, A., XTandem Parser: an open-source library to parse and analyse X!Tandem MS/MS search results. Proteomics 2010, 10, 1522-1524.
34. Colaert, N., Barsnes, H., Vaudel, M., Helsens, K. et al., Thermo-msf-parser: an open source Java library to parse and visualize Thermo Proteome Discoverer msf files. J. Proteome Res. 2011, 10, 3840-3843.
35. Ma, B., Zhang, K., Hendrie, C., Liang, C. et al., PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2003, 17, 2337-2342.
36. Barsnes, H., Eidhammer, I., Martens, L., FragmentationAnalyzer: an open-source tool to analyze MS/MS fragmentation data. Proteomics 2010, 10, 1087-1090.
37. Barsnes, H., Eidhammer, I., Martens, L., A global analysis of peptide fragmentation variability. Proteomics 2011, 11, 1181-1188.
38. Muth, T., Weilnbock, L., Rapp, E., Huber, C. G. et al., DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra. J. Proteome Res. 2014, 13, 1143-1146.
39. Nesvizhskii, A. I., Aebersold, R., Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics 2005, 4, 1419-1440.
40. Martens, L., Hermjakob, H., Jones, P., Adamski, M. et al., PRIDE: the proteomics identifications database. Proteomics 2005, 5, 3537-3545.
41. Baker, P. R., Chalkley, R. J., MS-viewer: a web-based spectral viewer for proteomics results. Mol. Cell Proteomics 2014, 13, 1392-1396.
42. Olsen, J. V., Mann, M., Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell Proteomics 2013, 12, 3444-3452.
43. MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M. et al., Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26, 966-968.
44. DeSouza, L. V., Siu, K. W., Mass spectrometry-based quantification. Clin. Biochem. 2013, 46, 421-431.
45. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367-1372.
46. Arntzen, M. O., Koehler, C. J., Barsnes, H., Berven, F. S. et al., IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J. Proteome Res. 2011, 10, 913-920.
47. Polpitiya, A. D., Qian, W. J., Jaitly, N., Petyuk, V. A. et al., DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24, 1556-1558.
48. Rigbolt, K. T., Vanselow, J. T., Blagoev, B., GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data. Mol. Cell Proteomics 2011, 10, O110. 007450.
49. Streit, M., Gehlenborg, N., Bar charts and box plots. Nat. Methods 2014, 11, 117.
50. Spitzer, M., Wildenhain, J., Rappsilber, J., Tyers, M., BoxPlotR: a web tool for generation of box plots. Nat. Methods 2014, 11, 121-122.
51. Ringner, M., What is principal component analysis? Nat. Biotechnol. 2008, 26, 303-304.
52. Lex, A., Gehlenborg, N., Points of view: Sets and intersections. Nat. Methods 2014, 11, 779.
53. Micallef, L., Rodgers, P., eulerAPE: drawing area-proportional 3-venn diagrams using ellipses. PLoS One 2014, 9, e101717.
54. Blake, J. A., Harris, M. A., The Gene Ontology (GO) project: structured vocabularies for molecular biology and their application to genome and expression analysis. Curr. Protoc. Bioinformatics 2008, Chapter 7, Unit 7.2. doi: 10.1002/0471250953.bi0702s23.
55. Bauer, S., Grossmann, S., Vingron, M., Robinson, P. N., Ontologizer 2.0-a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 2008, 24, 1650-1651.
56. Huang da, W., Sherman, B. T., Tan, Q., Kir, J. et al., DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35(Web Server issue), W169-W175.
57. Kanehisa, M., Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27-30.
58. Hermjakob, H., Montecchi-Palazzi, L., Lewington, C., Mudali, S. et al., IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004, 32(Database issue), D452-D455.
59. Xenarios, I., Rice, D. W., Salwinski, L., Baron, M. K. et al., DIP: the database of interacting proteins. Nucleic Acids Res. 2000, 28, 289-291.
60. Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M. et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41(Database issue), D808-D815.
61. Joshi-Tope, G., Gillespie, M., Vastrik, I., D'Eustachio, P. et al., Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33(Database issue), D428-D432.
62. Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C., Kanehisa, M., KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007, 35(Web Server issue), W182-W185.
63. Yamada, T., Letunic, I., Okuda, S., Kanehisa, M., Bork, P., iPath2.0: interactive pathway explorer. Nucleic Acids Res. 2011, 39(Web Server issue), W412-W415.
64. van Iersel, M. P., Kelder, T., Pico, A. R., Hanspers, K. et al., Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 2008, 9, 399.
65. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S. et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498-2504.
66. Saito, R., Smoot, M. E., Ono, K., Ruscheinski, J. et al., A travel guide to Cytoscape plugins. Nat. Methods 2012, 9, 1069-1076.
67. Maere, S., Heymans, K., Kuiper, M., BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005, 21, 3448-3449.
68. Aranda, B., Blankenburg, H., Kerrien, S., Brinkman, F. S. et al., PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 2011, 8, 528-529.
69. Vaudel, M., Venne, A. S., Berven, F. S., Zahedi, R. P. et al., Shedding light on black boxes in protein identification. Proteomics 2014, 14, 1001-1005.
70. Muller, T., Schrotter, A., Loosse, C., Helling, S. et al., Sense and nonsense of pathway analysis software in proteomics. J. Proteome Res. 2011, 10, 5398-5408.
71. Khatri, P., Sirota, M., Butte, A.J., Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 2012, 8, e1002375.
72. Wilhelm, M., Schlegl, J., Hahne, H., Moghaddas Gholami, A. et al., Mass-spectrometry-based draft of the human proteome. Nature 2014, 509, 582-587.
73. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S. et al., A draft map of the human proteome. Nature 2014, 509, 575-581.
74. Desiere, F., Deutsch, E. W., King, N. L., Nesvizhskii, A. I. et al., The PeptideAtlas project. Nucleic Acids Res. 2006, 34(Database issue), D655-D658.
75. Craig, R., Cortens, J. P., Beavis, R. C., Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 2004, 3, 1234-1242.
76. Lane, L., Argoud-Puy, G., Britan, A., Cusin, I. et al., neXtProt: a knowledge platform for human proteins. Nucleic Acids Res. 2012, 40(Database issue), D76-D83.
77. Guo, F., Wang, D., Liu, Z., Lu, L. et al., CAPER: a chromosome-assembled human proteome browsER. J. Proteome Res. 2013, 12, 179-186.
78. Paik, Y. K., Jeong, S. K., Omenn, G. S., Uhlen, M. et al., The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome. Nat. Biotechnol. 2012, 30, 221-223.
79. Vickaryous, M. K., Hall, B. K., Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest. Biol. Rev. Camb. Philos. Soc. 2006, 81, 425-455.
80. Ezkurdia, I., Vazquez, J., Valencia, A., Tress, M., Analyzing the first drafts of the human proteome. J. Proteome Res. 2014, 13, 3854-3855.
81. Guldbrandsen, A., Vethe, H., Farag, Y., Oveland, E. et al., In-depth characterization of the cerebrospinal fluid proteome displayed through the CSF Proteome Resource (CSF-PR). Mol. Cell Proteomics 2014, 13, 3152-3163.
82. Juan, A. Vizcaíno, Eric, W. Deutsch, Rui Wang, Attila Csordas et al., ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32, 223-226.
83. Teleman, J., Dowsey, A. W., Gonzalez-Galarza, F. F., Perkins, S. et al., Numerical compression schemes for proteomics mass spectrometry data. Mol. Cell Proteomics 2014, 13, 1537-1542.
84. Taylor, C. F., Paton, N. W., Lilley, K. S., Binz, P. A. et al., The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 2007, 25, 887-893.
85. Barsnes, H., Martens, L., Crowdsourcing in proteomics: public resources lead to better experiments. Amino Acids 2013, 44, 1129-1137.
86. Good, B. M., Su, A. I., Crowdsourcing for bioinformatics. Bioinformatics 2013, 29, 1925-1933.
87. Farley, P. C., Using the computer game "FoldIt" to entice students to explore external representations of protein structure in a biochemistry course for nonmajors. Biochem. Mol. Biol. Educ. 2013, 41, 56-57.
88. Eiben, C. B., Siegel, J. B., Bale, J. B., Cooper, S. et al., Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nat. Biotechnol. 2012, 30, 190-192.
89. Khatib, F., DiMaio, F., Cooper, S., Kazmierczyk, M. et al., Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nat. Struct. Mol. Biol. 2011, 18, 1175-1177.
90. Sturm, M., Bertsch, A., Gröpl, C., Hildebrandt, A. et al., OpenMS-an open-source software framework for mass spectrometry. BMC Bioinformatics 2008, 9, 163.